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RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives (2405.18406v3)

Published 28 May 2024 in cs.CV, cs.AI, and cs.CL

Abstract: Recent video generative models primarily rely on carefully written text prompts for specific tasks, like inpainting or style editing. They require labor-intensive textual descriptions for input videos, hindering their flexibility to adapt personal/raw videos to user specifications. This paper proposes RACCooN, a versatile and user-friendly video-to-paragraph-to-video generative framework that supports multiple video editing capabilities such as removal, addition, and modification, through a unified pipeline. RACCooN consists of two principal stages: Video-to-Paragraph (V2P) and Paragraph-to-Video (P2V). In the V2P stage, we automatically describe video scenes in well-structured natural language, capturing both the holistic context and focused object details. Subsequently, in the P2V stage, users can optionally refine these descriptions to guide the video diffusion model, enabling various modifications to the input video, such as removing, changing subjects, and/or adding new objects. The proposed approach stands out from other methods through several significant contributions: (1) RACCooN suggests a multi-granular spatiotemporal pooling strategy to generate well-structured video descriptions, capturing both the broad context and object details without requiring complex human annotations, simplifying precise video content editing based on text for users. (2) Our video generative model incorporates auto-generated narratives or instructions to enhance the quality and accuracy of the generated content. (3) RACCooN also plans to imagine new objects in a given video, so users simply prompt the model to receive a detailed video editing plan for complex video editing. The proposed framework demonstrates impressive versatile capabilities in video-to-paragraph generation, video content editing, and can be incorporated into other SoTA video generative models for further enhancement.

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Citations (3)
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Summary

  • The paper presents a novel V2P2V framework that auto-generates comprehensive narratives for unified video editing.
  • It employs multi-granular spatiotemporal pooling and an autoencoder system to accurately capture and modify video content based on text prompts.
  • Experimental results show a +9.4% human evaluation boost and a 49.7% FVD reduction in object removal tasks, validating its efficacy.

A Comprehensive Video-to-Paragraph-to-Video Editing Framework

The paper provides a detailed examination of a novel framework coined as Video-to-Paragraph-to-Video (V2P2V), presenting a significant stride in the domain of video editing and generation. The haLLMark of this work is its capability to both generate detailed video descriptions and use these narratives to facilitate comprehensive video content editing tasks, including object addition, removal, and modification, all within a unified pipeline.

Framework Overview and Methodology

The V2P2V approach is divided into two primary stages: Video-to-Paragraph (V2P) and Paragraph-to-Video (P2V).

  • V2P Stage: In the first stage, the framework automatically generates well-structured, detailed natural language descriptions from input video sequences. This is achieved through a novel multi-granular spatiotemporal pooling strategy that captures both holistic and localized video contexts. The use of smaller, coherent groups of pixels known as superpixels allows the model to comprehend and describe various objects and actions across different granularity levels, enhancing the richness and applicability of the generated narratives.
  • P2V Stage: Following the generation of detailed descriptions, users can refine these narratives to guide the video diffusion model for various content editing tasks. The model supports the addition, removal, and modification of video objects based on user-modified text prompts. This stage leverages an autoencoder system to encode masked video inputs and utilizes user instructions to produce the final edited video, ensuring the modified content adheres to the textual updates.

Key Contributions

The proposed framework differentiates itself from existing methodologies through several key contributions:

  1. Multi-Granular Spatiotemporal Pooling: This innovative pooling strategy captures diverse and detailed local contexts, overcoming limitations of traditional video LLMs that often miss critical scene details.
  2. Unified Inpainting-Based Video Editing: Unlike existing methods that specialize in singular tasks (e.g., object removal or attribute modification), the V2P2V framework integrates multiple video content editing capabilities within a single model, facilitated through detailed, auto-generated descriptions.
  3. VPLM Dataset: The framework introduces the Video Paragraph with Localized Mask (VPLM) dataset, encompassing 7.2K detailed video-paragraph descriptions and 5.5K object-level descriptions with masks, which substantially supports both training and evaluation.

Experimental Evaluation

The framework has been validated across several tasks and datasets, demonstrating its versatility and efficacy:

  • Video-to-Paragraph Generation: On tasks involving the generation of descriptive narratives from video content, the framework surpasses strong baselines, showing significant improvements. For example, on the YouCook2 dataset, it achieved a +9.4%p improvement in human evaluation over existing models.
  • Text-Based Video Content Editing: The P2V stage showed considerable enhancements in text-based video content editing tasks, significantly reducing the Fréchet Video Distance (FVD) and increasing Structural Similarity Index Measure (SSIM) scores compared to prior models.
    • Object Removal tasks saw improvements with relative FVD reductions up to 49.7%.
    • Object Addition tasks demonstrated robust results with localized detail preservation.
  • Compatibility with SoTA Models: The framework also enhances State-of-The-Art (SoTA) models. When integrated with TokenFlow and FateZero for inversion-based editing, and VideoCrafter and DynamiCrafter for conditional video generation, the framework provided notable improvements in relevant metrics, validating its scalability and utility.

Practical and Theoretical Implications

Practically, the V2P2V framework simplifies the video editing process, making it accessible to a broader range of users by removing the need for exhaustive video annotations and enabling complex scene modifications through intuitive textual inputs. Theoretically, this research advances the understanding of video generative models, presenting a robust approach to capturing and utilizing detailed video contexts.

Future Developments

Looking forward, further research may focus on enhancing the granularity and specificity of the superpixel segmentation, improving the model's ability to handle even more complex and dynamic scenes. Additionally, integrating more sophisticated user interfaces for real-time text-based video editing could broaden the framework's applicability beyond research settings into practical, everyday use.

In summary, the V2P2V framework paves the way for more accessible, detailed, and versatile video editing and generation, marking a significant contribution to the field of video generative models.

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